Machine Learning Scientist
Posted on 7/19/2023
INACTIVE
HEALTH[at]SCALE

11-50 employees

ML precision delivery healthcare company
Company Overview
HEALTH[at]SCALE is on a mission to usher in a new era of care that is proactive, personalized, and precise. HEALTH[at]SCALE is a healthcare machine intelligence company that uses proprietary advances in artificial intelligence and machine learning to optimize care delivery for individuals by empowering at-risk payers, employers and providers.
Data & Analytics
Social Impact
Cybersecurity
AI & Machine Learning
Financial Services
Consumer Goods

Company Stage

Series A

Total Funding

$16M

Founded

2015

Headquarters

San Jose, California

Growth & Insights
Headcount

6 month growth

-6%

1 year growth

-9%

2 year growth

38%
Locations
San Jose, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Python
Data Structures & Algorithms
Java
C/C++
CategoriesNew
AI & Machine Learning
Software Engineering
Requirements
  • PhD in Computer Science, Statistics or related technical field
  • Track record of publishing in top-tier machine learning and AI conferences and journals (e.g., NeurIPS, ICML, AAAI, ICDM, KDD, JMLR) with the ability to clearly describe the corresponding research contributions
  • 2+ years of experience with machine learning in academia or industry
  • Strong mathematical and conceptual understanding of machine learning and AI
  • Familiarity with modern machine learning and optimization toolkits
  • Strong proficiency in Python (preferred), Java or C/C++
  • Excellent communication skills
Responsibilities
  • Research machine learning and artificial intelligence advances in core product areas
  • Identify key opportunities to drive greater accuracy and actionability for the company's machine intelligence technologies
  • Design and implement new machine intelligence algorithms that support our mission of providing context-aware, real-time, and personalized healthcare decision-support
  • Investigate methodological advancements leading to improved computational efficiency and scalability of learning approaches
  • Design and conduct structured experiments to evaluate and improve new approaches
  • Work closely with engineers to implement advances into production-ready systems and applications
  • Engage with internal and external stakeholders to define opportunities and constraints